docs: def default():
    """Default configuration for PPO."""
    # General
    algorithm = algorithms.PPO
    num_agents = 30
    eval_episodes = 30
    use_gpu = False
    # Environment
    normalize_ranges = True
    # Network
    network = networks.feed_forward_gaussian
    weight_summaries = dict(
        all=r'.*', policy=r'.*/policy/.*', value=r'.*/value/.*')
    policy_layers = 200, 100
    value_layers = 200, 100
    init_output_factor = 0.1
    init_std = 0.35
    # Optimization
    update_every = 30
    update_epochs = 25
    optimizer = tf.train.AdamOptimizer
    learning_rate = 1e-4
    # Losses
    discount = 0.995
    kl_target = 1e-2
    kl_cutoff_factor = 2
    kl_cutoff_coef = 1000
    kl_init_penalty = 1
    return locals()